Order of arguments of RegularGridInterpolator and interp2d

It is a memorandum of crap.

I'm always confused by the order of the arguments of scipy.interpolate.RegularGridInterpolator and the grid specification, so I wrote the test code. The reason for the confusion is that scipy.interpolate.interp2d and so on seem to have a different order of arguments.

No, the argument for the grid of interp2d is two one-dimensional arrays, and the first argument of RegularGridInterpolator is a multidimensional array, right? If you say that things are different, that's right, and in the case of a multidimensional array, if x (l), y (m), z (n), the shape is (n, m, l). I have no words to return, but I just write it in reverse. <The procession is weak for a long time

So, if I shame this way, I'll make a mistake next time, praying and making a memorandum.

import numpy as np
from scipy.interpolate import RegularGridInterpolator

x_iter = np.array([0,1,2,3,4])
y_iter = np.array([5,6,7])
z_iter = np.array([8,9])
print("number of x_iter l = %d" % (len(x_iter)))
print("number of y_iter m = %d" % (len(y_iter)))
print("number of z_iter n = %d" % (len(z_iter)))

def data_func(x, y, z) :
    return x + y*10 + z*100

data = []
for z in z_iter :
    buf2 = []
    for y in y_iter :
        buf = []
        for x in x_iter :
            buf.append(data_func(x, y, z))
        buf2.append(buf)
    data.append(buf2)

data = np.array(data)
print("data", data)
print("shape of data, n x m x l array", data.shape)

data_grid = tuple([z_iter, y_iter, x_iter])
print("data grid with a format tuple(z_iter, y_iter, x_iter) is", data_grid)
f = RegularGridInterpolator(data_grid, data)

point_of_interest = tuple([8.3, 5.5, 1.1])
print("point_of_interest with a format tuple(z, y, x) is", point_of_interest)
interp_value = f(point_of_interest)

print("interpolated value is", interp_value)

import matplotlib
matplotlib.use("Agg")
import matplotlib.pyplot as plt
from matplotlib import colors

figid = 1
plt.figure(figid)
ax1 = plt.subplot(1, 1, 1)
xx, yy = np.meshgrid(x_iter, y_iter)

data_2D = data[0, :, :]
print("shape of data_2D for z_bin = 0", data_2D.shape)
phist = ax1.pcolormesh(xx, yy, data_2D, norm=colors.Normalize())
plt.colorbar(phist)
ax1.set_ylabel("y")
ax1.set_xlabel("x")
plt.savefig("interp_test.png ")

When creating data while turning with for, pay attention to the order of turning. In Default, if you specify a value outside the range for the x, y, z values, an error will occur. This behavior can be controlled with the bounds_error argument and the fill_value variable (default is bounds_error = True, fill_value = nan)

interp_test.png

By the way, when using scipy.interpolation.interp2D, it looks like this. By Default, if you specify a value outside the range for the x and y values, you will not die (returns the end value in the data table). This behavior can be controlled with the bounds_error argument and the fill_value variable (default is bounds_error = False, fill_value = None)

import numpy as np
from scipy.interpolate import interp2d

x_iter = np.array([0,1,2,3,4])
y_iter = np.array([5,6,7])
print("number of x_iter l = %d" % (len(x_iter)))
print("number of y_iter m = %d" % (len(y_iter)))

def data_func(x, y) :
    return x + y*10

data = []
for y in y_iter :
    buf = []
    for x in x_iter :
        buf.append(data_func(x, y))
    data.append(buf)

data = np.array(data)
print("data", data)
print("shape of data, m x l array", data.shape)

# order of arguments: x-axis, y-axis, data
f = interp2d(x_iter, y_iter, data)

x = 1.5
y = 5.5
print("datapoint is (%f, %f), data_func(%f, %f) is %f" % (x, y, x, y, data_func(x, y)))
interp_value = f(x, y)
print("interpolated value is", interp_value)

# test2 : what happens if x1 is out of range?
x1 = 7 # x-range is (0, 4)
y1 = 10 # y-range is (5, 7)
print("datapoint is (%f, %f), data_func(%f, %f) is %f" % (x1, y1, x1, y1, data_func(x1, y1)))
interp_value = f(x1, y1)
print("interpolated value is", interp_value)
# this value is 
print("interpolated value for (%f, %f) is %f" % (x_iter[-1], y_iter[-1], f(x_iter[-1], y_iter[-1])))

import matplotlib
matplotlib.use("Agg")
import matplotlib.pyplot as plt
from matplotlib import colors

figid = 1
plt.figure(figid)
ax1 = plt.subplot(1, 1, 1)
xx, yy = np.meshgrid(x_iter, y_iter)

phist = ax1.pcolormesh(xx, yy, data, norm=colors.Normalize())
plt.colorbar(phist)
ax1.set_ylabel("y")
ax1.set_xlabel("x")
plt.savefig("interp_test2D.png ")

Recommended Posts

Order of arguments of RegularGridInterpolator and interp2d
00. Reverse order of strings
Summary of Python arguments
Dictionary of keyword arguments
Reference order of class variables and instance variables in "self. Class variables" in Python
Problems of liars and honesty
Mechanism of pyenv and virtualenv
Pre-processing and post-processing of pytest
Combination of recursion and generator
Combination of anyenv and direnv
Explanation and implementation of SocialFoceModel
Differentiation of sort and generalization of sort
Coexistence of pyenv and autojump
Optional arguments and * args, ** kwargs
Use and integration of "Shodan"
Problems of liars and honesty
Occurrence and resolution of tensorflow.python.framework.errors_impl.FailedPreconditionError
Comparison of Apex and Lamvery
Source installation and installation of Python
Introduction and tips of mlflow.Tracking